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盖倞尧，男，辽宁鞍山人。学习工作经历如下：?2021.2至今，广西大学机械工程学院机电系，助理教授；?2020. 11，美国爱荷华州立大学，农业生物系统工程系，取得博士学位；?2016. 08，美国爱荷华州立大学，农业生物系统工程系，取得硕士学位；?2013. 06，中国科学技术大学，精密机械与精密仪器系，取得学士学位。 主要从事农业机器人的研发，以及机器视觉在农业生产、植物表型测量等方面的研究。具有丰富农业机器人开发技术经验。 在Journal of Field Robotics, Computer and Electronics in Agriculture, Transaction of ASABE等国际期刊发表、会议收录共7篇。本人曾多次在国际学术会议 (ASABE年会、Phenome等) 上做学术汇报演讲。曾任Agronomy, Robotics, Precision Agriculture, Journal of field robotics, Computers and electronics of agriculture 等期刊审稿人。
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主要从事农业机器人的研发，以及机器视觉在农业生产、植物表型测量等方面的研究。具有丰富农业机器人开发技术经验。
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在美国爱荷华州立大学攻读研究生、博士期间，参与完成以下项目： NSF MRI, Development of a PhenoNet – an Integrated Robotic Network for Field-based Studies of Genotype x Environment Interactions, 2016–2021 USDA NIFA, Developing Robotic Technologies for In-Situ Automated Intra-Row Weed Control for Vegetable Production Systems, 2013-2017
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1.Gai, J., Tuel T., Bao, Y., Xiang, L. & Tang, L. (2021). GPS-based Navigation Control for a Central-Articulated Robotic Vehicle. Computer and Electronics in Agriculture. Under review. 2.Gai, J., Xiang, L., & Tang, L. (2021). Robot In-Field Navigation based on Crop Row Detection using Discrete Fourier Transform (DFT). Computer and Electronics in Agriculture. Under review. 3.Gai, J., Xiang, L., & Tang, L. (2021). Using a Depth Camera for Visual Odometry and Field Mapping in Under-Canopy Agricultural Robotic Navigation. Computer and Electronics in Agriculture. Under review. 4.Xiang, L., Tang L., Gai J., & Wang L. (2021), PhenoStereo: a high-throughput stereo vision system for field-based plant phenotyping-with an application in sorghum stem diameter estimation. Transaction of ASABE. Under review.5.Xiang, L., Nolan T., Bao Y., Elmore M., Tuel T., Gai J., Shah D., Huser N., Hurd A., McLaughlin S., Howell S., Walley J., Yin Y., Tang L. (2020). Robotic Assay for Drought (RoAD): An Automated Phenotyping System for Brassinosteroid and Drought Response. bioRxiv. https://doi.org/10.1101/2020.06.01.1281996.Gai, J., Tang, L., & Steward, B. (2019). Automated Crop Plant Detection Based on the Fusion of Color and Depth Images for Robotic Weed Control. Journal of Field Robotics, rob.21897. https://doi.org/10.1002/rob.218977.Zhang, W., Gai, J., Zhang, Z., Tang, L., Ding, Y., & Liao, Q. (2019). Double-DQN-based path smoothing and tracking control method for in-field robotic vehicle navigation. Computer and Electronics in Agriculture, 166. https://doi.org/10.1016/j.compag.2019.104985 8.Tu, X., Gai, J., & Tang, L. (2019). Robust Navigation Control of a 4WD/4WS Agricultural Robotic Vehicle. Computer and Electronics in Agriculture, 164. https://doi.org/10.1016/j.compag.2019.1048929.Gai, J., Tang, L., & Steward, B. (2016). Plant localization and discrimination using 2D+3D computer vision for robotic intra-row weed control. In 2016 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers.10.Shah, D., Tang, L., Gai, J., & Putta-Venkata, R. (2016). Development of a mobile robotic phenotyping system for growth chamber-based studies of genotype x environment interactions. IFAC-PapersOnLine, 49(16), 248-253.11.Gai, J., Tang, L., & Steward, B. (2015). Plant recognition through the fusion of 2D and 3D images for robotic weeding. In 2015 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers. 12.Toledo, O. M., Steward, B. L., Tang, L., & Gai, J. (2014). Techno-economic analysis of future precision field robots. In 2014 Montreal, Quebec Canada July 13–July 16, 2014 (p. 1). American Society of Agricultural and Biological Engineers.
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1.Steward, B., Gai, J., & Tang, L. (2019). The use of agricultural robots in weed management and control. In J. Billingsley (Ed.), Robotics and automation for improving agriculture (1st ed.). Cambridge, England: Sawston.
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?1st place, 2020 AOCABFE Graduate Student Research Presentation Competition?2020 Research Excellence Award, Iowa State University
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